David Ping, Head of GenAI and ML Solution Architecture at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills.You'll learn about ML algorithms, cloud infrastructure, system design, MLOps, and how to apply ML to solve real-world business problems. David explains the generative AI project life cycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as generative AI, the biggest new addition to the handbook is the exploration of ML risk management and a deep understanding of the different stages of AI/ML adoption.By the end of this book, you’ll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You’ll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.